Systems and methods herein generally relate to obtaining image rendition preferences from users and more particularly to methods and systems that substantially limit the number of images needed to be reviewed by the user and that retrieve previously stored user preferences based on classes of images.
Digital printing provides the ability to have short-run jobs at much lower page-cost than traditional analog (e.g., lithographic) printing. This enables a significant market segment called one-to-one (1-1) printing for highly personalized print jobs. Examples are photo books and book on-demand. For photo books, color rendition is known to be a factor for customer satisfaction. Current practices address color rendition via “opinion of majority” or “expert opinion.” However, color rendition is highly subjective, and individual customers have their own preferences. For critical jobs, artists may work with complicated workflows that use very precise soft-proofing or hard-proofing techniques. However, non-professional photo book customers are generally simply provided an average “best” color rendition according to the opinion of the majority of users.
Thus, preference of color rendition is very subjective. Common approaches either ignore individual color preference by using a rendition which captures opinion of the majority only, or through user-performed extensive/complicated soft-proofing for the entire document. The former does not address individual preferences, while the later is time-consuming and can be frustrating. Neither approach is suitable for typical consumer of 1-1 printing applications such as ordering photo books or ordering prints of family pictures. Here 1-1 printing refers to print jobs with contents that are specific for a single user. This is to contrast with the kind of print jobs wherein many users receive copies of same or similar contents.